Articles | Volume 19, issue 11
Nat. Hazards Earth Syst. Sci., 19, 2513–2524, 2019

Special issue: Hydroclimatic extremes and impacts at catchment to regional...

Nat. Hazards Earth Syst. Sci., 19, 2513–2524, 2019

Research article 13 Nov 2019

Research article | 13 Nov 2019

Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

Leila Goodarzi et al.

Related authors

Scaling issues in multi-criteria evaluation of combinations of measures for integrated river basin management
Jörg Dietrich
Proc. IAHS, 373, 19–24,,, 2016
Short summary

Related subject area

Hydrological Hazards
Drought propagation and its impact on groundwater hydrology of wetlands: a case study on the Doode Bemde nature reserve (Belgium)
Buruk Kitachew Wossenyeleh, Kaleb Asnake Worku, Boud Verbeiren, and Marijke Huysmans
Nat. Hazards Earth Syst. Sci., 21, 39–51,,, 2021
Short summary
Modelling the Brumadinho tailings dam failure, the subsequent loss of life and how it could have been reduced
Darren Lumbroso, Mark Davison, Richard Body, and Gregor Petkovšek
Nat. Hazards Earth Syst. Sci., 21, 21–37,,, 2021
Short summary
Assessment of probability distributions and analysis of the minimum storage draft rate in the equatorial region
Hasrul Hazman Hasan, Siti Fatin Mohd Razali, Nur Shazwani Muhammad, and Firdaus Mohamad Hamzah
Nat. Hazards Earth Syst. Sci., 21, 1–19,,, 2021
Short summary
Downsizing parameter ensembles for simulations of rare floods
Anna E. Sikorska-Senoner, Bettina Schaefli, and Jan Seibert
Nat. Hazards Earth Syst. Sci., 20, 3521–3549,,, 2020
Short summary
Dynamic maps of human exposure to floods based on mobile phone data
Matteo Balistrocchi, Rodolfo Metulini, Maurizio Carpita, and Roberto Ranzi
Nat. Hazards Earth Syst. Sci., 20, 3485–3500,,, 2020
Short summary

Cited articles

Abebe, A. and Price, R.: Decision support system for urban flood management, J. Hydroinform., 7, 3–15,, 2005. 
Aichouri, I., Hani, A., Bougherira, N., Djabri, L., Chaffai, H., and Lallahem, S.: River flow model using artificial neural networks, Energy Proced., 74, 1007–1014,, 2015. 
Amirkhani, H. and Rahmati, M.: Expectation maximization based ordering aggregation for improving the K2 structure learning algorithm, Intell. Data Anal., 19, 1003–1018,, 2015. 
ASCE: Task Committee on Application of Artificial Neural Networks in Hydrology: Artificial neural networks in hydrology. II: hydrologic applications, J. Hydrol. Eng., 5, 124–137, 2000. 
Banihabib, M. and Arabi, A.: The impact of catchment management on emergency management of flash-flood, International Journal of Emergency Management, 12, 185–195,, 2016. 
Short summary
We developed a novel approach in using Bayesian networks (BNs) for ensemble flood forecasting in a case study in Iran. This allows fast early warning without the need for hydrological modelling. We recommend to combine precipitation ensembles with hydrological initial conditions in the BN. The number of observed flood events is low by nature. Under the limited amount of data, BN outperformed artificial neural networks with good results. Future work will validate the concept further.
Final-revised paper